Overview

Dataset statistics

Number of variables15
Number of observations87
Missing cells78
Missing cells (%)6.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.3 KiB
Average record size in memory121.5 B

Variable types

Numeric14
Categorical1

Alerts

Results is highly correlated with Reach and 3 other fieldsHigh correlation
Reach is highly correlated with Results and 5 other fieldsHigh correlation
Impressions is highly correlated with Results and 5 other fieldsHigh correlation
Cost per results is highly correlated with Results and 1 other fieldsHigh correlation
Amount spent (MXN) is highly correlated with Reach and 5 other fieldsHigh correlation
Result rate is highly correlated with Results and 1 other fieldsHigh correlation
New messaging connections is highly correlated with Reach and 3 other fieldsHigh correlation
Messaging Conversations Started is highly correlated with Reach and 3 other fieldsHigh correlation
Frequency is highly correlated with Reach and 2 other fieldsHigh correlation
CPC (All) (MXN) is highly correlated with Amount spent (MXN)High correlation
Results is highly correlated with Result rateHigh correlation
Reach is highly correlated with Impressions and 3 other fieldsHigh correlation
Impressions is highly correlated with Reach and 3 other fieldsHigh correlation
Amount spent (MXN) is highly correlated with Reach and 2 other fieldsHigh correlation
Result rate is highly correlated with ResultsHigh correlation
New messaging connections is highly correlated with Reach and 3 other fieldsHigh correlation
Messaging Conversations Started is highly correlated with Reach and 2 other fieldsHigh correlation
Results is highly correlated with Messaging Conversations StartedHigh correlation
Reach is highly correlated with Impressions and 3 other fieldsHigh correlation
Impressions is highly correlated with Reach and 3 other fieldsHigh correlation
Cost per results is highly correlated with Result rateHigh correlation
Amount spent (MXN) is highly correlated with Reach and 4 other fieldsHigh correlation
Result rate is highly correlated with Cost per resultsHigh correlation
New messaging connections is highly correlated with Reach and 3 other fieldsHigh correlation
Messaging Conversations Started is highly correlated with Results and 4 other fieldsHigh correlation
Frequency is highly correlated with Amount spent (MXN)High correlation
Starts is highly correlated with Ends and 1 other fieldsHigh correlation
Ends is highly correlated with StartsHigh correlation
Ad set budget is highly correlated with Result rate and 3 other fieldsHigh correlation
Results is highly correlated with Reach and 2 other fieldsHigh correlation
Reach is highly correlated with Results and 5 other fieldsHigh correlation
Impressions is highly correlated with Results and 4 other fieldsHigh correlation
Cost per results is highly correlated with CPC (All) (MXN)High correlation
Amount spent (MXN) is highly correlated with Starts and 4 other fieldsHigh correlation
Result rate is highly correlated with Ad set budget and 2 other fieldsHigh correlation
New messaging connections is highly correlated with Reach and 3 other fieldsHigh correlation
Messaging Conversations Started is highly correlated with Ad set budget and 4 other fieldsHigh correlation
Frequency is highly correlated with Result rateHigh correlation
CPC (All) (MXN) is highly correlated with Ad set budget and 1 other fieldsHigh correlation
CTR (all) is highly correlated with Ad set budget and 1 other fieldsHigh correlation
Results has 12 (13.8%) missing values Missing
Cost per results has 12 (13.8%) missing values Missing
Result rate has 13 (14.9%) missing values Missing
New messaging connections has 23 (26.4%) missing values Missing
Messaging Conversations Started has 18 (20.7%) missing values Missing
Reach has 8 (9.2%) zeros Zeros
Impressions has 8 (9.2%) zeros Zeros
Cost per results has 1 (1.1%) zeros Zeros
Amount spent (MXN) has 8 (9.2%) zeros Zeros
Frequency has 8 (9.2%) zeros Zeros
CPC (All) (MXN) has 9 (10.3%) zeros Zeros
CTR (all) has 9 (10.3%) zeros Zeros

Reproduction

Analysis started2022-04-20 02:39:42.627034
Analysis finished2022-04-20 02:40:01.301588
Duration18.67 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Campaign ID
Real number (ℝ≥0)

Distinct82
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean546.4022989
Minimum121
Maximum997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size824.0 B
2022-04-19T21:40:01.411612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum121
5-th percentile132.6
Q1289
median544
Q3798.5
95-th percentile963.4
Maximum997
Range876
Interquartile range (IQR)509.5

Descriptive statistics

Standard deviation275.7441289
Coefficient of variation (CV)0.5046540425
Kurtosis-1.252728351
Mean546.4022989
Median Absolute Deviation (MAD)262
Skewness0.02785109742
Sum47537
Variance76034.82465
MonotonicityNot monotonic
2022-04-19T21:40:01.525638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4402
 
2.3%
9952
 
2.3%
1822
 
2.3%
4652
 
2.3%
1262
 
2.3%
3411
 
1.1%
7911
 
1.1%
9071
 
1.1%
9191
 
1.1%
2481
 
1.1%
Other values (72)72
82.8%
ValueCountFrequency (%)
1211
1.1%
1262
2.3%
1271
1.1%
1321
1.1%
1341
1.1%
1351
1.1%
1441
1.1%
1541
1.1%
1751
1.1%
1822
2.3%
ValueCountFrequency (%)
9971
1.1%
9952
2.3%
9681
1.1%
9641
1.1%
9621
1.1%
9561
1.1%
9381
1.1%
9281
1.1%
9211
1.1%
9191
1.1%

Starts
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.459770115
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size824.0 B
2022-04-19T21:40:01.633662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.105596288
Coefficient of variation (CV)0.7519723727
Kurtosis-1.45208762
Mean5.459770115
Median Absolute Deviation (MAD)3
Skewness0.4478453355
Sum475
Variance16.85592088
MonotonicityNot monotonic
2022-04-19T21:40:01.704678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
117
19.5%
215
17.2%
1114
16.1%
311
12.6%
68
9.2%
127
8.0%
56
 
6.9%
105
 
5.7%
92
 
2.3%
41
 
1.1%
ValueCountFrequency (%)
117
19.5%
215
17.2%
311
12.6%
41
 
1.1%
56
 
6.9%
68
9.2%
71
 
1.1%
92
 
2.3%
105
 
5.7%
1114
16.1%
ValueCountFrequency (%)
127
8.0%
1114
16.1%
105
 
5.7%
92
 
2.3%
71
 
1.1%
68
9.2%
56
 
6.9%
41
 
1.1%
311
12.6%
215
17.2%

Ends
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.701149425
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size824.0 B
2022-04-19T21:40:01.787697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.997426331
Coefficient of variation (CV)0.7011614733
Kurtosis-1.465909544
Mean5.701149425
Median Absolute Deviation (MAD)2
Skewness0.4431426903
Sum496
Variance15.97941727
MonotonicityNot monotonic
2022-04-19T21:40:01.859714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
220
23.0%
313
14.9%
1113
14.9%
128
 
9.2%
18
 
9.2%
67
 
8.0%
107
 
8.0%
55
 
5.7%
43
 
3.4%
73
 
3.4%
ValueCountFrequency (%)
18
 
9.2%
220
23.0%
313
14.9%
43
 
3.4%
55
 
5.7%
67
 
8.0%
73
 
3.4%
107
 
8.0%
1113
14.9%
128
 
9.2%
ValueCountFrequency (%)
128
 
9.2%
1113
14.9%
107
 
8.0%
73
 
3.4%
67
 
8.0%
55
 
5.7%
43
 
3.4%
313
14.9%
220
23.0%
18
 
9.2%

Ad set budget
Categorical

HIGH CORRELATION

Distinct21
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Memory size824.0 B
Using ad set budget
29 
500
10 
300
10 
1000
200
Other values (16)
26 

Length

Max length19
Median length4
Mean length8.540229885
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)9.2%

Sample

1st rowUsing ad set budget
2nd rowUsing ad set budget
3rd rowUsing ad set budget
4th rowUsing ad set budget
5th rowUsing ad set budget

Common Values

ValueCountFrequency (%)
Using ad set budget29
33.3%
50010
 
11.5%
30010
 
11.5%
10007
 
8.0%
2005
 
5.7%
3503
 
3.4%
20003
 
3.4%
15002
 
2.3%
1002
 
2.3%
4002
 
2.3%
Other values (11)14
16.1%

Length

2022-04-19T21:40:01.939737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
using29
16.7%
set29
16.7%
budget29
16.7%
ad29
16.7%
50010
 
5.7%
30010
 
5.7%
10007
 
4.0%
2005
 
2.9%
3503
 
1.7%
20003
 
1.7%
Other values (14)20
11.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Results
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct46
Distinct (%)61.3%
Missing12
Missing (%)13.8%
Infinite0
Infinite (%)0.0%
Mean387.76
Minimum1
Maximum16326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size824.0 B
2022-04-19T21:40:02.030764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median17
Q343
95-th percentile1316.6
Maximum16326
Range16325
Interquartile range (IQR)37

Descriptive statistics

Standard deviation1944.376325
Coefficient of variation (CV)5.014380867
Kurtosis63.18845889
Mean387.76
Median Absolute Deviation (MAD)14
Skewness7.730455776
Sum29082
Variance3780599.293
MonotonicityNot monotonic
2022-04-19T21:40:02.134781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
15
 
5.7%
34
 
4.6%
104
 
4.6%
84
 
4.6%
53
 
3.4%
23
 
3.4%
93
 
3.4%
63
 
3.4%
173
 
3.4%
432
 
2.3%
Other values (36)41
47.1%
(Missing)12
 
13.8%
ValueCountFrequency (%)
15
5.7%
23
3.4%
34
4.6%
42
 
2.3%
53
3.4%
63
3.4%
71
 
1.1%
84
4.6%
93
3.4%
104
4.6%
ValueCountFrequency (%)
163261
1.1%
37571
1.1%
22631
1.1%
16611
1.1%
11691
1.1%
5871
1.1%
5051
1.1%
3991
1.1%
2951
1.1%
2931
1.1%

Reach
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct80
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5641.367816
Minimum0
Maximum41768
Zeros8
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size824.0 B
2022-04-19T21:40:02.256830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11221
median3657
Q37639.5
95-th percentile17733.5
Maximum41768
Range41768
Interquartile range (IQR)6418.5

Descriptive statistics

Standard deviation6796.188388
Coefficient of variation (CV)1.204705775
Kurtosis9.236924449
Mean5641.367816
Median Absolute Deviation (MAD)2794
Skewness2.557660414
Sum490799
Variance46188176.61
MonotonicityNot monotonic
2022-04-19T21:40:02.359853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08
 
9.2%
25111
 
1.1%
52761
 
1.1%
20481
 
1.1%
40811
 
1.1%
14021
 
1.1%
22181
 
1.1%
84171
 
1.1%
42851
 
1.1%
421
 
1.1%
Other values (70)70
80.5%
ValueCountFrequency (%)
08
9.2%
421
 
1.1%
1911
 
1.1%
3951
 
1.1%
5231
 
1.1%
5401
 
1.1%
6201
 
1.1%
6891
 
1.1%
7001
 
1.1%
7251
 
1.1%
ValueCountFrequency (%)
417681
1.1%
256641
1.1%
237761
1.1%
205641
1.1%
178041
1.1%
175691
1.1%
143761
1.1%
137501
1.1%
135361
1.1%
133581
1.1%

Impressions
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct80
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7472.931034
Minimum0
Maximum55206
Zeros8
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size824.0 B
2022-04-19T21:40:02.458886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11514
median4527
Q39533
95-th percentile24923.9
Maximum55206
Range55206
Interquartile range (IQR)8019

Descriptive statistics

Standard deviation9174.974191
Coefficient of variation (CV)1.227761122
Kurtosis8.367787355
Mean7472.931034
Median Absolute Deviation (MAD)3650
Skewness2.472016888
Sum650145
Variance84180151.41
MonotonicityNot monotonic
2022-04-19T21:40:02.549907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08
 
9.2%
30721
 
1.1%
81771
 
1.1%
29021
 
1.1%
52831
 
1.1%
15371
 
1.1%
28251
 
1.1%
99371
 
1.1%
60921
 
1.1%
421
 
1.1%
Other values (70)70
80.5%
ValueCountFrequency (%)
08
9.2%
421
 
1.1%
1961
 
1.1%
3951
 
1.1%
5381
 
1.1%
6341
 
1.1%
6611
 
1.1%
7151
 
1.1%
7491
 
1.1%
9931
 
1.1%
ValueCountFrequency (%)
552061
1.1%
340671
1.1%
295351
1.1%
282501
1.1%
253641
1.1%
238971
1.1%
228061
1.1%
222431
1.1%
200191
1.1%
178511
1.1%

Cost per results
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct74
Distinct (%)98.7%
Missing12
Missing (%)13.8%
Infinite0
Infinite (%)0.0%
Mean17.16460531
Minimum0
Maximum118.1957143
Zeros1
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size824.0 B
2022-04-19T21:40:02.647928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0784661873
Q14.718406593
median11.62790698
Q322.93363636
95-th percentile59.94883333
Maximum118.1957143
Range118.1957143
Interquartile range (IQR)18.21522977

Descriptive statistics

Standard deviation19.57396203
Coefficient of variation (CV)1.140367732
Kurtosis9.672232912
Mean17.16460531
Median Absolute Deviation (MAD)8.686730509
Skewness2.634775576
Sum1287.345398
Variance383.1399896
MonotonicityNot monotonic
2022-04-19T21:40:02.881051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.666666672
 
2.3%
10.50251
 
1.1%
18.868751
 
1.1%
69.7721
 
1.1%
3.1251
 
1.1%
1.571
 
1.1%
6.3021
 
1.1%
0.3723443221
 
1.1%
8.81
 
1.1%
24.444444441
 
1.1%
Other values (64)64
73.6%
(Missing)12
 
13.8%
ValueCountFrequency (%)
01
1.1%
0.0435494881
1.1%
0.0496049251
1.1%
0.061953161
1.1%
0.0855431991
1.1%
0.0862521291
1.1%
0.0924540861
1.1%
0.1774059411
1.1%
0.2000842871
1.1%
0.3389830511
1.1%
ValueCountFrequency (%)
118.19571431
1.1%
69.7721
1.1%
66.77251
1.1%
65.551
1.1%
57.548333331
1.1%
39.043529411
1.1%
38.39751
1.1%
34.3171
1.1%
32.021
1.1%
31.951
1.1%

Amount spent (MXN)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct70
Distinct (%)80.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean232.6009195
Minimum0
Maximum1000
Zeros8
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size824.0 B
2022-04-19T21:40:02.983956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q148.455
median145.29
Q3348.105
95-th percentile786.208
Maximum1000
Range1000
Interquartile range (IQR)299.65

Descriptive statistics

Standard deviation242.1726101
Coefficient of variation (CV)1.041150699
Kurtosis1.009047938
Mean232.6009195
Median Absolute Deviation (MAD)122.33
Skewness1.284634332
Sum20236.28
Variance58647.57307
MonotonicityNot monotonic
2022-04-19T21:40:03.084980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08
 
9.2%
5006
 
6.9%
1004
 
4.6%
140.22
 
2.3%
3802
 
2.3%
332.341
 
1.1%
2701
 
1.1%
59.031
 
1.1%
17.61
 
1.1%
168.041
 
1.1%
Other values (60)60
69.0%
ValueCountFrequency (%)
08
9.2%
1.571
 
1.1%
10.751
 
1.1%
12.761
 
1.1%
16.571
 
1.1%
17.61
 
1.1%
23.141
 
1.1%
26.491
 
1.1%
31.511
 
1.1%
31.951
 
1.1%
ValueCountFrequency (%)
10001
 
1.1%
887.411
 
1.1%
827.371
 
1.1%
809.851
 
1.1%
787.31
 
1.1%
783.661
 
1.1%
663.741
 
1.1%
633.21
 
1.1%
607.571
 
1.1%
5006
6.9%

Result rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct74
Distinct (%)100.0%
Missing13
Missing (%)14.9%
Infinite0
Infinite (%)0.0%
Mean4.137558289
Minimum0.035078488
Maximum74.17721519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size824.0 B
2022-04-19T21:40:03.192003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.035078488
5-th percentile0.081628135
Q10.1515175448
median0.274435813
Q30.5872112327
95-th percentile11.64044754
Maximum74.17721519
Range74.1421367
Interquartile range (IQR)0.435693688

Descriptive statistics

Standard deviation13.99209232
Coefficient of variation (CV)3.381726936
Kurtosis19.59746779
Mean4.137558289
Median Absolute Deviation (MAD)0.1382532515
Skewness4.495280686
Sum306.1793134
Variance195.7786474
MonotonicityNot monotonic
2022-04-19T21:40:03.298028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1378359751
 
1.1%
0.1301236171
 
1.1%
0.2831858411
 
1.1%
0.0503169971
 
1.1%
1.0505581091
 
1.1%
2.3809523811
 
1.1%
0.310752021
 
1.1%
5.2298850571
 
1.1%
0.2300834051
 
1.1%
0.2670226971
 
1.1%
Other values (64)64
73.6%
(Missing)13
 
14.9%
ValueCountFrequency (%)
0.0350784881
1.1%
0.0503169971
1.1%
0.0634920631
1.1%
0.0683860881
1.1%
0.0887584681
1.1%
0.0929476011
1.1%
0.0961538461
1.1%
0.1104484211
1.1%
0.1110288681
1.1%
0.1148908541
1.1%
ValueCountFrequency (%)
74.177215191
1.1%
71.586424631
1.1%
65.066129961
1.1%
12.777352721
1.1%
11.028267831
1.1%
11.010938021
1.1%
9.5091527621
1.1%
7.4604816071
1.1%
7.18635811
1.1%
5.2298850571
1.1%

New messaging connections
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)42.2%
Missing23
Missing (%)26.4%
Infinite0
Infinite (%)0.0%
Mean15.046875
Minimum1
Maximum116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size824.0 B
2022-04-19T21:40:03.396048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median8
Q322.25
95-th percentile40
Maximum116
Range115
Interquartile range (IQR)19.25

Descriptive statistics

Standard deviation18.55017194
Coefficient of variation (CV)1.232825549
Kurtosis12.9834101
Mean15.046875
Median Absolute Deviation (MAD)6
Skewness2.938898062
Sum963
Variance344.108879
MonotonicityNot monotonic
2022-04-19T21:40:03.477844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
18
 
9.2%
36
 
6.9%
26
 
6.9%
64
 
4.6%
93
 
3.4%
83
 
3.4%
103
 
3.4%
73
 
3.4%
403
 
3.4%
263
 
3.4%
Other values (17)22
25.3%
(Missing)23
26.4%
ValueCountFrequency (%)
18
9.2%
26
6.9%
36
6.9%
42
 
2.3%
51
 
1.1%
64
4.6%
73
 
3.4%
83
 
3.4%
93
 
3.4%
103
 
3.4%
ValueCountFrequency (%)
1161
 
1.1%
511
 
1.1%
481
 
1.1%
403
3.4%
391
 
1.1%
361
 
1.1%
331
 
1.1%
321
 
1.1%
291
 
1.1%
271
 
1.1%

Messaging Conversations Started
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct32
Distinct (%)46.4%
Missing18
Missing (%)20.7%
Infinite0
Infinite (%)0.0%
Mean17.10144928
Minimum1
Maximum121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size824.0 B
2022-04-19T21:40:03.568869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median9
Q323
95-th percentile53.2
Maximum121
Range120
Interquartile range (IQR)20

Descriptive statistics

Standard deviation20.5477528
Coefficient of variation (CV)1.201521138
Kurtosis8.878494335
Mean17.10144928
Median Absolute Deviation (MAD)7
Skewness2.503211861
Sum1180
Variance422.2101449
MonotonicityNot monotonic
2022-04-19T21:40:03.660027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
17
 
8.0%
36
 
6.9%
25
 
5.7%
54
 
4.6%
104
 
4.6%
84
 
4.6%
63
 
3.4%
93
 
3.4%
173
 
3.4%
292
 
2.3%
Other values (22)28
32.2%
(Missing)18
20.7%
ValueCountFrequency (%)
17
8.0%
25
5.7%
36
6.9%
42
 
2.3%
54
4.6%
63
3.4%
72
 
2.3%
84
4.6%
93
3.4%
104
4.6%
ValueCountFrequency (%)
1211
1.1%
651
1.1%
641
1.1%
561
1.1%
491
1.1%
432
2.3%
421
1.1%
411
1.1%
381
1.1%
362
2.3%

Frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct79
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.170927552
Minimum0
Maximum2.213877
Zeros8
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size824.0 B
2022-04-19T21:40:03.763121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.090525
median1.212693
Q31.372782
95-th percentile1.7404124
Maximum2.213877
Range2.213877
Interquartile range (IQR)0.282257

Descriptive statistics

Standard deviation0.4386805971
Coefficient of variation (CV)0.3746436716
Kurtosis2.845484436
Mean1.170927552
Median Absolute Deviation (MAD)0.154365
Skewness-1.339504037
Sum101.870697
Variance0.1924406663
MonotonicityNot monotonic
2022-04-19T21:40:03.856119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08
 
9.2%
12
 
2.3%
1.1035671
 
1.1%
1.4169921
 
1.1%
1.2945361
 
1.1%
1.0962911
 
1.1%
1.273671
 
1.1%
1.1805871
 
1.1%
1.4217041
 
1.1%
1.1882541
 
1.1%
Other values (69)69
79.3%
ValueCountFrequency (%)
08
9.2%
12
 
2.3%
1.0060031
 
1.1%
1.0064521
 
1.1%
1.0214291
 
1.1%
1.0225811
 
1.1%
1.0261781
 
1.1%
1.0286811
 
1.1%
1.0331031
 
1.1%
1.0353911
 
1.1%
ValueCountFrequency (%)
2.2138771
1.1%
2.0203191
1.1%
1.9746441
1.1%
1.765441
1.1%
1.7643291
1.1%
1.6846071
1.1%
1.6449931
1.1%
1.6256481
1.1%
1.5498481
1.1%
1.5488591
1.1%

CPC (All) (MXN)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct79
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.048275046
Minimum0
Maximum10.7
Zeros9
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size824.0 B
2022-04-19T21:40:03.955154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.337426
median0.682813
Q31.0735725
95-th percentile2.4130448
Maximum10.7
Range10.7
Interquartile range (IQR)0.7361465

Descriptive statistics

Standard deviation1.699669893
Coefficient of variation (CV)1.621396884
Kurtosis20.92144389
Mean1.048275046
Median Absolute Deviation (MAD)0.366637
Skewness4.398133444
Sum91.199929
Variance2.888877745
MonotonicityNot monotonic
2022-04-19T21:40:04.056286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09
 
10.3%
0.5001591
 
1.1%
1.2486991
 
1.1%
0.8301891
 
1.1%
0.5571
 
1.1%
0.8879411
 
1.1%
1.8169791
 
1.1%
0.31251
 
1.1%
0.39251
 
1.1%
0.3332791
 
1.1%
Other values (69)69
79.3%
ValueCountFrequency (%)
09
10.3%
0.0653171
 
1.1%
0.0742381
 
1.1%
0.1136621
 
1.1%
0.1357141
 
1.1%
0.1483681
 
1.1%
0.1667711
 
1.1%
0.1671791
 
1.1%
0.1736241
 
1.1%
0.2759621
 
1.1%
ValueCountFrequency (%)
10.71
1.1%
9.6205811
1.1%
7.7091
1.1%
2.749021
1.1%
2.4621051
1.1%
2.2985711
1.1%
2.0052571
1.1%
1.8794121
1.1%
1.8169791
1.1%
1.7438891
1.1%

CTR (all)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct79
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.988235609
Minimum0
Maximum18.461538
Zeros9
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size824.0 B
2022-04-19T21:40:04.156310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5359305
median4.755245
Q36.1143855
95-th percentile13.740369
Maximum18.461538
Range18.461538
Interquartile range (IQR)3.578455

Descriptive statistics

Standard deviation3.83244521
Coefficient of variation (CV)0.7682967507
Kurtosis2.798938627
Mean4.988235609
Median Absolute Deviation (MAD)1.698938
Skewness1.435802975
Sum433.976498
Variance14.68763629
MonotonicityNot monotonic
2022-04-19T21:40:04.249344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09
 
10.3%
3.9154751
 
1.1%
4.2384561
 
1.1%
5.0160891
 
1.1%
5.8555631
 
1.1%
6.0176991
 
1.1%
1.9321731
 
1.1%
10.5055811
 
1.1%
9.523811
 
1.1%
5.8429121
 
1.1%
Other values (69)69
79.3%
ValueCountFrequency (%)
09
10.3%
0.2557541
 
1.1%
0.8401721
 
1.1%
1.0335921
 
1.1%
1.2179491
 
1.1%
1.466361
 
1.1%
1.8111371
 
1.1%
1.9321731
 
1.1%
2.0378221
 
1.1%
2.214121
 
1.1%
ValueCountFrequency (%)
18.4615381
1.1%
16.7340691
1.1%
16.4190011
1.1%
15.4298311
1.1%
14.8941931
1.1%
11.0481131
1.1%
10.5055811
1.1%
10.0558661
1.1%
9.523811
1.1%
8.7443951
1.1%

Interactions

2022-04-19T21:39:59.426754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:44.917435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:45.943122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:47.212735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:48.274968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:49.527003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:50.567227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:51.766822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:52.800066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:53.951907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:55.104874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:56.135150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:57.291647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:58.283611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:59.500770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:45.002865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:46.018153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:47.290740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:48.353999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:49.597013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:50.638244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:51.844848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:52.995143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:54.022102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:55.171937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:56.205601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:57.361012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:58.349637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:59.577772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:45.070886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:46.095170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:47.368758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:48.434016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:49.673030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:50.715261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:51.915867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:53.069160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:54.096729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:55.242952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:56.280620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:57.430037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:58.419642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:59.648798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:45.137900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:46.169169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:47.443783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:48.509020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:49.748045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:50.786277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:51.987885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:53.136180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:54.163742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:55.316967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:56.349633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:57.494248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:58.486685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:59.730819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:45.209927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:46.253206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:47.525808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:48.591387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:49.829071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:50.992322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:52.066902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:53.213197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:54.245782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:55.392985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:56.430554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:57.570061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:58.564667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:59.803832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:45.276928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:46.327205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:47.597834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:48.665395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:49.896079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:51.060337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:52.134918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:53.280231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:54.314803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:55.460000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:56.497985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:57.635083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:58.628700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:59.880843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:45.349942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:46.404228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:47.672847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:48.740419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:49.969103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:51.128353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:52.207932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:53.357279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:54.383811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:55.532009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:56.569001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:57.703092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:58.820724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:59.962874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:45.418974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:46.481256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:47.748864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:48.955648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:50.042112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:51.216660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:52.277948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:53.428295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:54.455827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:55.611019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:56.644013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:57.774118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:58.898742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:40:00.038890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:45.486989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:46.556262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:47.824886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:49.035665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:50.116128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:51.306668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:52.347964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:53.507305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:54.528943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:55.683056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:56.715020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:57.849133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:58.977760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:40:00.115896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:45.562990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:46.635291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:47.902886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:49.115683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:50.195145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:51.391750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:52.423980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:53.586324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:54.604966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:55.760067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:56.925068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:57.934142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:59.052789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:40:00.191109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:45.637051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:46.721292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:47.977920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:49.200701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:50.269164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:51.469768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:52.503000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:53.656339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:54.677984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:55.834069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:56.999585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:58.007164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:59.125810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:40:00.282132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:45.712085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:46.970669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:48.052931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:49.284943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:50.350179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:51.547788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:52.578002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:53.733368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:54.753000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:55.914096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:57.074599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:58.078182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:59.202810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:40:00.367152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:45.784085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:47.046685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:48.121952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:49.361960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:50.419197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:51.614801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:52.646034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:53.802881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:54.956368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:55.989104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:57.142615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:58.143189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:59.275703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:40:00.450183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:45.864103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:47.125716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:48.195968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:49.441978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:50.491213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:51.686816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:52.721038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:53.874895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:55.029845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:56.062143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:57.214632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:58.211200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T21:39:59.346719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-04-19T21:40:04.459898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-19T21:40:04.592941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-19T21:40:04.724958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-19T21:40:04.864988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-19T21:40:00.718242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-19T21:40:00.926276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-19T21:40:01.107545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-19T21:40:01.209569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Campaign IDStartsEndsAd set budgetResultsReachImpressionsCost per resultsAmount spent (MXN)Result rateNew messaging connectionsMessaging Conversations StartedFrequencyCPC (All) (MXN)CTR (all)
03410102Using ad set budgetNaN00NaN0.00NaNNaNNaN0.0000000.0000000.000000
19950102Using ad set budgetNaN00NaN0.00NaNNaNNaN0.0000000.0000000.000000
29640102Using ad set budgetNaN00NaN0.00NaNNaNNaN0.0000000.0000000.000000
38490202Using ad set budgetNaN00NaN0.00NaNNaNNaN0.0000000.0000000.000000
41820202Using ad set budgetNaN00NaN0.00NaNNaNNaN0.0000000.0000000.000000
56630202Using ad set budget2.0000.0000000.00NaNNaN2.00.0000000.0000000.000000
69280303Using ad set budget15.0251130725.56000083.400.4882819.015.01.2234170.5380655.045573
72640303Using ad set budget8.0140816567.47000059.760.4830928.08.01.1761360.4299288.393720
84650303Using ad set budget36.0133581748012.796667460.680.20595029.036.01.3085790.5432554.851259
98160304Using ad set budget17.04203590615.742353267.620.28784313.017.01.4051870.8036645.638334

Last rows

Campaign IDStartsEndsAd set budgetResultsReachImpressionsCost per resultsAmount spent (MXN)Result rateNew messaging connectionsMessaging Conversations StartedFrequencyCPC (All) (MXN)CTR (all)
7741611111000NaN620634NaN26.49NaNNaNNaN1.0225810.7358335.678233
78625111150022.05454771922.727273500.000.28501122.022.01.4152920.9596936.749579
79135111150030.07150920516.666667500.000.32591026.030.01.2874131.0162605.344921
808691111160NaN11201345NaN50.25NaNNaNNaN1.2008930.8104844.609665
8146511115006.03657540457.548333345.290.1110296.06.01.4777141.7438893.663953
827721111300NaN191196NaN16.57NaNNaNNaN1.0261781.3808336.122449
83126010120010.02435354816.065000160.650.28184910.010.01.4570840.8238465.496054
8449901013008.02894397025.201250201.610.2015118.08.01.3718040.9081535.591940
85548010130013.03285392017.327692225.260.331633NaN13.01.1933032.2985712.500000
86531030350026.08124984211.015385286.400.26417421.026.01.2114720.5693845.110750